Summary of Large Language Models Are Advanced Anonymizers, by Robin Staab et al.
Large Language Models are Advanced Anonymizers
by Robin Staab, Mark Vero, Mislav Balunović, Martin Vechev
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new approach to anonymizing online text data is proposed, addressing the gap between existing methods and regulatory requirements. The paper introduces a novel setting for evaluating anonymization performance in the face of adversarial large language model (LLM) inferences. A LLM-based adversarial anonymization framework is developed, leveraging the strong inferential capabilities of LLMs to inform anonymization procedures. Experimental evaluation across 13 LLMs on real-world and synthetic texts shows that this approach outperforms current commercial anonymizers in terms of utility and privacy. Human studies (n=50) confirm a strong preference for LLM-anonymized text. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has created a new way to protect people’s personal information found online. They want to make sure that when we share information, it’s safe from being understood by computers or other machines. The team made a special setting to test their method and found that it works better than the methods used today. They also did some experiments with different computer models and real-life text data. This new approach helps keep our information private and also makes sure we can still understand what’s written. |
Keywords
» Artificial intelligence » Large language model
